The present invention relates generally to processing data captured on sensors, and more particularly to digitally processing images captured on pixel array sensors.
Modern image sensors, such as typical CCD and CMOS sensors found in cameras, typically use arrays of rectangular pixels.
From the early 1960s, however, hexagonal sampling has been known as the optimal sampling approach for isotropically band-limited two dimensional images, providing a 13.4% improvement in sampling efficiency over rectangular sampling. Hexagonal sampling provides a 13.4% improvement in sampling efficiency, meaning that an image can be represented with fewer hexagonal pixels than would be required for a rectangular representation. A hexagonal grid also has consistent connectivity since all neighboring pixels share a side. Therefore, there is no connectivity ambiguity as there is with rectangular grids, which leads to more efficient algorithms that deal with connectivity. A hexagonal grid also has greater angular resolution, equidistant spacing and a higher degree of symmetry than rectangular grids.
Moreover, as shown, for example, in many insect compound eyes and even cones in a human retina, even nature appears to recognize the advantages of hexagonal sensor arrangements.
Despite the advantages of hexagonal sampling and nature's examples, rectangular sampling is still used for virtually all digital imaging applications. Part of the reason for this is that rectangular sampling leads to nice rectangular arrays that are easy to process and store on digital computers, whereas hexagonal sampling, at least in the prior art, does not. Several addressing approaches have been developed over the years to try to remedy this, but none have matched the efficiency and convenience of a rectangular array. Simply put, no prior art efficient addressing method for hexagonal grids has been developed. For example, none of the prior art methods support efficient linear algebra and image processing manipulation. As a result, the processing overhead required to deal with addressing hexagonal sensor arrays or grids has thus far outweighed the advantages gained by sampling hexagonally.
There is, therefore, a need for a new method for addressing hexagonally arranged image sensors that produces an output that can be efficiently computationally manipulated, particularly in digital systems.
There is also a need for a new method for addressing hexagonally arranged data gathering or sampling elements generally that provides data that can be manipulated straightforwardly and efficiently.